#73 Temple (11-11)

avg: 1480.87  •  sd: 58.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
85 Richmond Win 11-7 1896.59 Jan 25th Carolina Kickoff 2019
94 Appalachian State Win 9-8 1497.43 Jan 26th Carolina Kickoff 2019
55 Florida State Loss 9-11 1362.47 Jan 26th Carolina Kickoff 2019
11 North Carolina State Loss 3-13 1427.57 Jan 26th Carolina Kickoff 2019
85 Richmond Loss 10-11 1304.7 Jan 27th Carolina Kickoff 2019
102 Georgetown Loss 11-14 1037.85 Jan 27th Carolina Kickoff 2019
98 Kansas Win 13-12 1488.18 Feb 8th Florida Warm Up 2019
127 Boston College Win 12-7 1795.23 Feb 8th Florida Warm Up 2019
20 Tufts Loss 6-13 1264.15 Feb 8th Florida Warm Up 2019
136 South Florida Win 10-5 1810.93 Feb 9th Florida Warm Up 2019
65 Florida Win 15-9 2051.23 Feb 9th Florida Warm Up 2019
48 Kennesaw State Loss 6-11 1099.79 Feb 9th Florida Warm Up 2019
43 Harvard Loss 3-12 1072.28 Feb 9th Florida Warm Up 2019
150 Cornell Win 14-11 1491.42 Feb 10th Florida Warm Up 2019
54 Virginia Tech Loss 10-12 1381.32 Feb 10th Florida Warm Up 2019
47 Maryland Loss 9-12 1310.96 Mar 16th Oak Creek Invite 2019
150 Cornell Win 11-7 1644.98 Mar 16th Oak Creek Invite 2019
157 Drexel Win 13-7 1686.94 Mar 16th Oak Creek Invite 2019
197 George Mason Win 13-6 1601.39 Mar 16th Oak Creek Invite 2019
66 Penn State Loss 10-15 1081.64 Mar 17th Oak Creek Invite 2019
18 Michigan Loss 9-13 1490.2 Mar 17th Oak Creek Invite 2019
163 SUNY-Geneseo Win 15-7 1706.58 Mar 17th Oak Creek Invite 2019
**Blowout Eligible

FAQ

The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)